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Merge remote-tracking branch 'upstream/master' into pandas.io.sas.sasreader
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‎RELEASE.md

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=============
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The list of changes to Pandas between each release can be found
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[here](http://pandas.pydata.org/pandas-docs/stable/whatsnew.html). For full
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[here](https://pandas.pydata.org/pandas-docs/stable/whatsnew/index.html). For full
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details, see the commit logs at http://github.com/pandas-dev/pandas.

‎asv_bench/asv.conf.json

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"project": "pandas",
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// The project's homepage
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"project_url": "http://pandas.pydata.org/",
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"project_url": "https://pandas.pydata.org/",
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// The URL of the source code repository for the project being
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// benchmarked

‎asv_bench/benchmarks/categoricals.py

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import numpy as np
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import pandas as pd
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import pandas.util.testing as tm
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import pandas._testing as tm
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try:
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from pandas.api.types import union_categoricals

‎asv_bench/benchmarks/ctors.py

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import numpy as np
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from pandas import DatetimeIndex, Index, MultiIndex, Series, Timestamp
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import pandas.util.testing as tm
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import pandas._testing as tm
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def no_change(arr):

‎asv_bench/benchmarks/frame_ctor.py

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import numpy as np
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from pandas import DataFrame, MultiIndex, Series, Timestamp, date_range
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import pandas.util.testing as tm
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import pandas._testing as tm
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try:
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from pandas.tseries.offsets import Nano, Hour

‎asv_bench/benchmarks/frame_methods.py

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import numpy as np
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from pandas import DataFrame, MultiIndex, NaT, Series, date_range, isnull, period_range
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import pandas.util.testing as tm
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import pandas._testing as tm
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class GetNumericData:

‎asv_bench/benchmarks/gil.py

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import numpy as np
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from pandas import DataFrame, Series, date_range, factorize, read_csv
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import pandas._testing as tm
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from pandas.core.algorithms import take_1d
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import pandas.util.testing as tm
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try:
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from pandas import (
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except ImportError:
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from pandas import algos
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try:
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from pandas.util.testing import test_parallel
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from pandas._testing import test_parallel
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have_real_test_parallel = True
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except ImportError:

‎asv_bench/benchmarks/groupby.py

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date_range,
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period_range,
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)
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import pandas.util.testing as tm
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import pandas._testing as tm
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method_blacklist = {
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"object": {

‎asv_bench/benchmarks/index_object.py

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Series,
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date_range,
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)
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import pandas.util.testing as tm
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import pandas._testing as tm
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class SetOperations:

‎asv_bench/benchmarks/indexing.py

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option_context,
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period_range,
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)
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import pandas.util.testing as tm
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import pandas._testing as tm
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class NumericSeriesIndexing:

‎asv_bench/benchmarks/inference.py

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import numpy as np
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from pandas import DataFrame, Series, to_numeric
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import pandas.util.testing as tm
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import pandas._testing as tm
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from .pandas_vb_common import lib, numeric_dtypes
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‎asv_bench/benchmarks/io/csv.py

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import numpy as np
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from pandas import Categorical, DataFrame, date_range, read_csv, to_datetime
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import pandas.util.testing as tm
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import pandas._testing as tm
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from ..pandas_vb_common import BaseIO
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‎asv_bench/benchmarks/io/excel.py

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from odf.text import P
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from pandas import DataFrame, ExcelWriter, date_range, read_excel
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import pandas.util.testing as tm
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import pandas._testing as tm
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def _generate_dataframe():

‎asv_bench/benchmarks/io/hdf.py

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import numpy as np
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from pandas import DataFrame, HDFStore, date_range, read_hdf
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import pandas.util.testing as tm
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import pandas._testing as tm
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from ..pandas_vb_common import BaseIO
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‎asv_bench/benchmarks/io/json.py

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import numpy as np
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from pandas import DataFrame, concat, date_range, read_json, timedelta_range
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import pandas.util.testing as tm
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import pandas._testing as tm
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from ..pandas_vb_common import BaseIO
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‎asv_bench/benchmarks/io/pickle.py

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import numpy as np
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from pandas import DataFrame, date_range, read_pickle
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import pandas.util.testing as tm
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import pandas._testing as tm
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from ..pandas_vb_common import BaseIO
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‎asv_bench/benchmarks/io/sql.py

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from sqlalchemy import create_engine
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from pandas import DataFrame, date_range, read_sql_query, read_sql_table
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import pandas.util.testing as tm
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import pandas._testing as tm
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class SQL:

‎asv_bench/benchmarks/io/stata.py

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import numpy as np
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from pandas import DataFrame, date_range, read_stata
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import pandas.util.testing as tm
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import pandas._testing as tm
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from ..pandas_vb_common import BaseIO
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‎asv_bench/benchmarks/join_merge.py

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import numpy as np
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from pandas import DataFrame, MultiIndex, Series, concat, date_range, merge, merge_asof
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import pandas.util.testing as tm
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import pandas._testing as tm
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from pandas import merge_ordered

‎asv_bench/benchmarks/multiindex_object.py

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import numpy as np
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from pandas import DataFrame, MultiIndex, RangeIndex, date_range
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import pandas.util.testing as tm
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import pandas._testing as tm
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class GetLoc:

‎asv_bench/benchmarks/reindex.py

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import numpy as np
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from pandas import DataFrame, Index, MultiIndex, Series, date_range, period_range
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import pandas.util.testing as tm
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import pandas._testing as tm
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from .pandas_vb_common import lib
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‎asv_bench/benchmarks/series_methods.py

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import numpy as np
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from pandas import NaT, Series, date_range
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import pandas.util.testing as tm
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import pandas._testing as tm
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class SeriesConstructor:

‎asv_bench/benchmarks/strings.py

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import numpy as np
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from pandas import DataFrame, Series
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import pandas.util.testing as tm
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import pandas._testing as tm
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class Methods:

‎ci/code_checks.sh

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RET=$(($RET + $?)) ; echo $MSG "DONE"
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# Checks for test suite
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# Check for imports from pandas.util.testing instead of `import pandas.util.testing as tm`
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invgrep -R --include="*.py*" -E "from pandas.util.testing import" pandas/tests
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# Check for imports from pandas._testing instead of `import pandas._testing as tm`
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invgrep -R --include="*.py*" -E "from pandas._testing import" pandas/tests
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RET=$(($RET + $?)) ; echo $MSG "DONE"
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invgrep -R --include="*.py*" -E "from pandas.util import testing as tm" pandas/tests
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RET=$(($RET + $?)) ; echo $MSG "DONE"

‎conda.recipe/meta.yaml

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about:
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home: http://pandas.pydata.org
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home: https://pandas.pydata.org
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license: BSD

‎doc/source/development/code_style.rst

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.. _code_style:
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{{ header }}
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=======================
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pandas code style guide
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=======================
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.. contents:: Table of contents:
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:local:
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Patterns
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========
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foo.__class__
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-------------
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*pandas* uses 'type(foo)' instead 'foo.__class__' as it is making the code more
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readable.
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For example:
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**Good:**
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.. code-block:: python
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foo = "bar"
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type(foo)
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**Bad:**
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.. code-block:: python
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foo = "bar"
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foo.__class__
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String formatting
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=================
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Concatenated strings
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--------------------
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f-strings
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~~~~~~~~~
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*pandas* uses f-strings formatting instead of '%' and '.format()' string formatters.
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The convention of using f-strings on a string that is concatenated over serveral lines,
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is to prefix only the lines containing the value needs to be interpeted.
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For example:
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**Good:**
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.. code-block:: python
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foo = "old_function"
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bar = "new_function"
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my_warning_message = (
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f"Warning, {foo} is deprecated, "
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"please use the new and way better "
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f"{bar}"
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)
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**Bad:**
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.. code-block:: python
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foo = "old_function"
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bar = "new_function"
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my_warning_message = (
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f"Warning, {foo} is deprecated, "
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f"please use the new and way better "
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f"{bar}"
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)
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White spaces
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~~~~~~~~~~~~
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Putting the white space only at the end of the previous line, so
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there is no whitespace at the beggining of the concatenated string.
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For example:
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**Good:**
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.. code-block:: python
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example_string = (
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"Some long concatenated string, "
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"with good placement of the "
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"whitespaces"
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)
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**Bad:**
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.. code-block:: python
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example_string = (
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"Some long concatenated string,"
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" with bad placement of the"
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" whitespaces"
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)
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Representation function (aka 'repr()')
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--------------------------------------
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*pandas* uses 'repr()' instead of '%r' and '!r'.
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The use of 'repr()' will only happend when the value is not an obvious string.
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For example:
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**Good:**
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.. code-block:: python
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value = str
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f"Unknown recived value, got: {repr(value)}"
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**Good:**
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.. code-block:: python
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value = str
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f"Unknown recived type, got: '{type(value).__name__}'"

‎doc/source/development/contributing.rst

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summary of the API documentation. And also validate common errors in the docstring
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of a specific class, function or method. The summary also compares the list of
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methods documented in ``doc/source/api.rst`` (which is used to generate
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the `API Reference <http://pandas.pydata.org/pandas-docs/stable/api.html>`_ page)
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the `API Reference <https://pandas.pydata.org/pandas-docs/stable/api.html>`_ page)
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and the actual public methods.
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This will identify methods documented in ``doc/source/api.rst`` that are not actually
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class methods, and existing methods that are not documented in ``doc/source/api.rst``.
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a lot of user code as a result, that is, we need it to be as *backwards compatible*
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as possible to avoid mass breakages.
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Additional standards are outlined on the `code style wiki
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page <https://github.com/pandas-dev/pandas/wiki/Code-Style-and-Conventions>`_.
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Additional standards are outlined on the `pandas code style guide <code_style>`_
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Optional dependencies
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---------------------
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network connectivity, there is more information on the `testing page
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<https://github.com/pandas-dev/pandas/wiki/Testing>`_ of the wiki.
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The ``pandas.util.testing`` module has many special ``assert`` functions that
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The ``pandas._testing`` module has many special ``assert`` functions that
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make it easier to make statements about whether Series or DataFrame objects are
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equivalent. The easiest way to verify that your code is correct is to
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explicitly construct the result you expect, then compare the actual result to
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.. code-block:: python
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import pandas.util.testing as tm
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import pandas._testing as tm
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df = pd.DataFrame()

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